model architecture

2026-07-03 — quoting @recurseparadox (Pranav Shyam), with reply from @synquid (Rasmus)

@cloneofsimo (Simo Ryu) Reality is "Simplicity is the king" is such normie thing to say. Frontier systems are rarely ever "simple". [Embedded photo: a large industrial semiconductor lithography machine (ASML-branded, "ASM" visible on wall) in a cleanroom, two people in white cleanroom suits standing beside it for scale.] > QUOTED: @recurseparadox (Pranav Shyam) — Jul 2, replying to @OfirPress: This is mostly a matter of poor tooling and bad hyperparamter setups. A complex model can be as much as 10x more effective size if done without confounders. Era of dumb scaling is more over by the ... [platform truncated] 5:08 AM · Jul 3, 2026 · 11.6K Views [9 replies, 11 reposts, 155 likes, 24 bookmarks] @synquid (Rasmus) — 6h Simplicity is a crutch for monkey brains to understand things.

Note from Claude Sonnet 5

Tweet with an embedded real photograph of an ASML EUV lithography machine in a cleanroom, illustrating the "frontier systems are complex" argument; quoted tweet is platform-truncated.

ai scaling semiconductors model architecture engineering complexity twitter

Shannon San... @max_paperclips

2025-04-29 — reply from Lisan al Gaib (@scaling01)

Shannon Sands @max_paperclips the not-really-proven-but-plausible explanation is that depth = better reasoning (ie, deeper networks are better function approximators). Depth for reasoning, girth for recall 10:38 PM · Apr 29, 2025 · 19 Views [1 reply, 2 likes] Lisan al Gaib @scaling01 · 1m yep fucking girth had me dead

Note from Claude Sonnet 5

A casual technical exchange positing that network depth correlates with reasoning ability while width ("girth") correlates with recall/memorization — an informal architecture heuristic in ML Twitter discourse, tangential to Nathan's own neural architecture work (brain_graph_1 depth-vs-width experiments).

twitter neural networks model architecture depth vs width ml discourse